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Thompson, Bruce; Borrello, Gloria M. – Educational and Psychological Measurement, 1985
Multiple regression analysis is frequently being employed in experimental and non-experimental research. However, when data include predictor variables that are correlated, some regression results can become difficult to interpret. This paper presents a study to provide a demonstration that structure coefficients may be useful in these cases.…
Descriptors: Correlation, Multiple Regression Analysis, Multivariate Analysis, Predictor Variables
Thayer, Jerome D. – 1986
The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…
Descriptors: Comparative Analysis, Computer Simulation, Mathematical Models, Multiple Regression Analysis
Pohlmann, John T. – 1979
Three procedures used to control Type I error rate in stepwise regression analysis are forward selection, backward elimination, and true stepwise. In the forward selection method, a model of the dependent variable is formed by choosing the single best predictor; then the second predictor which makes the strongest contribution to the prediction of…
Descriptors: Computer Programs, Error Patterns, Mathematical Models, Multiple Regression Analysis
Robey, Randall R.; Barcikowski, Robert S. – 1986
This paper reports the results of a Monte Carlo investigation of Type I errors in the single group repeated measures design where multiple measures are collected from each observational unit at each measurement occasion. The Type I error of three multivariate tests were examined. These were the doubly multivariate F test, the multivariate mixed…
Descriptors: Analysis of Variance, Behavioral Science Research, Comparative Analysis, Hypothesis Testing
Mislevy, Robert J. – 1985
A method for drawing inferences from complex samples is based on Rubin's approach to missing data in survey research. Standard procedures for drawing such inferences do not apply when the variables of interest are not observed directly, but must be inferred from secondary random variables which depend on the variables of interest stochastically.…
Descriptors: Algorithms, Data Interpretation, Estimation (Mathematics), Latent Trait Theory
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Mayer, Matthew J. – Journal of School Violence, 2004
Methodological challenges associated with structural equation modeling (SEM) and structured means modeling (SMM) in research on school violence and related topics in the social and behavioral sciences are examined. Problems associated with multiyear implementations of large-scale surveys are discussed. Complex sample designs, part of any…
Descriptors: Violence, Structural Equation Models, Research Methodology, Measures (Individuals)
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Kleban, Morton H. – 1978
Q-type factor analysis was used to re-analyze baseline data collected in 1957, on 47 men aged 65-91. Q-type analysis is the use of factor methods to study persons rather than tests. Although 550 variables were originally studied involving psychiatry, medicine, cerebral metabolism and chemistry, personality, audiometry, dichotic and diotic memory,…
Descriptors: Biological Influences, Data Analysis, Factor Analysis, Factor Structure
Moore, R. P.; Shah, B. V. – 1975
An efficiency study was conducted of the sampling design used for the National Longitudinal Study of the High School Class of 1972 (NLS), and the change variables used in the first followup. Nine alternative designs were compared, using a cost model based upon the number of schools and students sampled. Fourteen change variables and eight domains…
Descriptors: Attitude Change, Cost Effectiveness, Data Analysis, Efficiency